DA0-002 Data Analysis Practice Question
A data analyst is preparing a dataset for a predictive model. The dataset contains a feature 'age' with values ranging from 18 to 80, and a feature 'income' ranging from 20,000 to 200,000. To ensure both features contribute equally to distance-based algorithms, which transformation should the analyst apply?
⚠ Common exam trap
A common mix-up: candidates confuse min-max normalization with standardization, assuming that scaling to a fixed range is sufficient for distance-based algorithms, without considering the impact of outliers or the need for zero mean and unit variance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Standardization (z-score)
Standardization (z-score) transforms features to have a mean of 0 and a standard deviation of 1, which ensures that both 'age' (18–80) and 'income' (20,000–200,000) contribute equally to distance-based algorithms like k-NN or k-means. Unlike min-max normalization, standardization is not affected by outliers and preserves the relative distances between data points, making it the preferred choice when the data does not follow a uniform distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max normalization
Why it's wrong here
Min-max scales to [0,1] but can be skewed by outliers, and does not guarantee equal contribution.
- ✗
Log transformation
Why it's wrong here
Log transformation is used to reduce skewness, not for scaling features.
- ✓
Standardization (z-score)
Why this is correct
Standardization ensures each feature has mean 0 and std 1, providing equal weight in distance calculations.
- ✗
Box-Cox transformation
Why it's wrong here
Box-Cox is for making data more normal, not for equal weighting.
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